For a fair comparison to previous work, the Inception network with batch normalization [1]} pre-trained on ILSVRC2020-CLS [2]} is adopted as our backbone. We add a 512-d fully-connected layer as a projection head following the global pooling layer and L2-normalize the final output. All input images are first resized to \(256 \times 256\) , and then cropped to \(224 \times 224\) . Random crops and random flips are utilized as data augmentation during training. The test set was preprocessed just by the center crop. For all experiments, we use Adam optimizer with \(5 e^{-4}\)  weight decay and the PK sampler (8 categories, 4 samples/category) during training. The implementation is done using the PyTorch framework, and experiments are performed on compute clusters containing P100 and V100. We utilized the default hyperparameters for contrastive loss, triplet loss and MS loss.
